In the utilities industries, a large amount of data may need to be gathered, analyzed, and reported. It may be important that the data be generated and analyzed very accurately, because the data may be used to determine usage and/or billing amounts for utilities customers. Often, many points of data will exist for a single customer, which must be aggregated and analyzed. Further, data must be aggregated and analyzed at a macro level in order to apportion usage for common resources and analyze transmission losses.
For example, FIG. 1 depicts a high-level overview of a production, transmission, and consumption model for a utility, such as electricity. The utility for a particular area is generated or produced by one or more utilities providers 110, 112, 114. The utility is typically transmitted over common transmission mediums 120 in order to reach consumers 130, 140, 150. Because utilities are generally fungible and because they are placed on a common transmission medium, it is difficult or impossible to track the actual units of the utility (e.g., electrons in power lines). Although a utility provider may know how much of a certain utility the provider placed on the common transmission medium, the utility provider may not have accurate information about the utility once the utility leaves the provider's control.
Accordingly, utilities customers are often monitored to determine the amount of their utility consumption. For example, consumers may be provided with devices such as meters 132, 142, 152 that record utility usage over a period of time. By monitoring the meters 132, 142, 152, the utilities providers 110, 112, 114 can determine how much of the utility the consumers 130, 140, 150 used.
For example, if utility provider 110 provides a utility to two consumers 130 and 140, the provider 110 may check the meters 132 and 142 in order to determine how much of the utility each consumer 130, 140 consumed over a given period of time. If meter 132 shows that consumer 130 consumed 5 units of electricity and meter 142 shows that consumer 140 consumed 10 units of electricity, each of the consumers 130, 140 can be billed for the respective amounts of electricity consumed.
In most situations, there is not a one-to-one correspondence between the amount of a utility placed on the common transmission medium 120 by a utility provider and the amount of a utility retrieved by the utility provider's consumers. For example, some of the utility may be lost in transmission. Because each of the utility providers 110, 112, 114 will suffer some amount of transmission loss, the amount of loss must be apportioned between the providers 110, 112, 114 in order to ensure that each provider supplies an amount of the utility sufficient to provide for the requirements of the utility provider's customers. The difficulty in accounting for large amounts of a fungible utility in a common pool compounds attempts to accurately obtain and analyze information about the utility.
Different circumstances between and within geographical areas also make it difficult to obtain and analyze utilities data. For example, there are a number of different types of meters available, each of which may report usage data in a different way. Two examples of meter types are meters that report a scalar set of values and meters that use interval data recorders that record utilities information over a fixed interval. Different weather types affect rates of transmission loss, and different jurisdictions have different reporting requirements by which utilities providers must abide. Data may also be stored in different formats depending on the utility provider.
Practicality also complicates data collection, analysis, and reporting. A single utility provider may be associated with many hundreds of thousands of consumers. It may be difficult or impossible to accurately measure the amount of utility consumed by each consumer by regularly checking each meter. Instead, utility consumption is often apportioned based on a usage profile. Meter readings are taken at dispersed intervals, and the meter readings are applied to a profile that defines typical usage requirements and conditions. The profile may therefore provide an estimation of utilities usage based on limited data. Different utility providers use different types of profiles, and different profiles may be employed for different customers of a single utilities provider.
These complications can make data aggregation and reporting difficult. Typically, because of the wide variety of different local conditions and requirements, a data aggregation or reporting solution that works for one utility provider may not be adequate or even functional for another utility provider. Accordingly, custom data aggregation and reporting systems and software must often be created for each utility provider depending on the utility provider's unique needs. Generating these custom systems and software may require great time and expense.
Traditional data aggregation implementations typically follow standard implementation patterns. An implementation team gathers and consolidates requirements, analyzes those requirements in order to develop a project plan, and works through design, development, testing, migration, and post-production phases of the plan. While this process allows for custom solutions that are tailored to the specific needs of each client and project, the process generally requires a lengthy implementation time before arriving at a solution.